Exploratory Data Analysis (EDA)

1 Introduction

Exploratory Data Analysis (EDA) is one of the fundamental steps in any data science process. It allows us to understand the structure, detect anomalies, and uncover patterns in the data before modeling.

“Without EDA, you’re not doing data science, you’re just guessing.”

EDA combines statistics, programming, and visualization to explore datasets. This report is designed to help you practice these core skills using real-world data.

1.1 Dataset

We will use the movies dataset from vega-datasets, which includes information about thousands of films such as their ratings, genres, duration, and box office revenue.

Let’s load and preview the dataset:

import pandas as pd
import altair as alt
from vega_datasets import data

# Load dataset
movies = data.movies()

# Show first rows
movies.head()

	Title	US_Gross	Worldwide_Gross	US_DVD_Sales	Production_Budget	Release_Date	MPAA_Rating	Running_Time_min	Distributor	Source	Major_Genre	Creative_Type	Director	Rotten_Tomatoes_Rating	IMDB_Rating	IMDB_Votes
0	The Land Girls	146083.0	146083.0	NaN	8000000.0	Jun 12 1998	R	NaN	Gramercy	None	None	None	None	NaN	6.1	1071.0
1	First Love, Last Rites	10876.0	10876.0	NaN	300000.0	Aug 07 1998	R	NaN	Strand	None	Drama	None	None	NaN	6.9	207.0
2	I Married a Strange Person	203134.0	203134.0	NaN	250000.0	Aug 28 1998	None	NaN	Lionsgate	None	Comedy	None	None	NaN	6.8	865.0
3	Let's Talk About Sex	373615.0	373615.0	NaN	300000.0	Sep 11 1998	None	NaN	Fine Line	None	Comedy	None	None	13.0	NaN	NaN
4	Slam	1009819.0	1087521.0	NaN	1000000.0	Oct 09 1998	R	NaN	Trimark	Original Screenplay	Drama	Contemporary Fiction	None	62.0	3.4	165.0

Now, let’s examine the shape (number of rows and columns) of the dataset:

movies.shape

(3201, 16)

This tells us how many entries (rows) and features (columns) are present in the dataset.

1.2 First Steps

Before diving deeper into the data, it’s useful to explore some key metadata:

• ✅ The column names and their data types
• ⚠️ The presence of missing values
• 📊 Summary statistics for numeric columns

1.2.1 Column Names and Data Types

Understanding the structure of the dataset helps us know what type of data we’re dealing with.

movies.dtypes

Title                      object
US_Gross                  float64
Worldwide_Gross           float64
US_DVD_Sales              float64
Production_Budget         float64
Release_Date               object
MPAA_Rating                object
Running_Time_min          float64
Distributor                object
Source                     object
Major_Genre                object
Creative_Type              object
Director                   object
Rotten_Tomatoes_Rating    float64
IMDB_Rating               float64
IMDB_Votes                float64
dtype: object

We can also use .info() for a more complete summary, including non-null counts:

# Overview of the dataset
movies.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3201 entries, 0 to 3200
Data columns (total 16 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   Title                   3200 non-null   object 
 1   US_Gross                3194 non-null   float64
 2   Worldwide_Gross         3194 non-null   float64
 3   US_DVD_Sales            564 non-null    float64
 4   Production_Budget       3200 non-null   float64
 5   Release_Date            3201 non-null   object 
 6   MPAA_Rating             2596 non-null   object 
 7   Running_Time_min        1209 non-null   float64
 8   Distributor             2969 non-null   object 
 9   Source                  2836 non-null   object 
 10  Major_Genre             2926 non-null   object 
 11  Creative_Type           2755 non-null   object 
 12  Director                1870 non-null   object 
 13  Rotten_Tomatoes_Rating  2321 non-null   float64
 14  IMDB_Rating             2988 non-null   float64
 15  IMDB_Votes              2988 non-null   float64
dtypes: float64(8), object(8)
memory usage: 400.3+ KB

1.3 Missing Values

Detecting and handling missing values is a critical step in any EDA process. Missing data can bias analysis or break downstream models if not handled properly.

• Detect patterns in missingness
• Identify if some columns are almost entirely null
• Decide whether to drop or impute certain variables

1.3.1 Percentage of Missing Values per Column

Let’s start by computing the percentage of missing values in each column:

nan_percent = movies.isna().mean() * 100
nan_percent_sorted = nan_percent.sort_values(ascending=False).round(2)
nan_percent_sorted

US_DVD_Sales              82.38
Running_Time_min          62.23
Director                  41.58
Rotten_Tomatoes_Rating    27.49
MPAA_Rating               18.90
Creative_Type             13.93
Source                    11.40
Major_Genre                8.59
Distributor                7.25
IMDB_Rating                6.65
IMDB_Votes                 6.65
US_Gross                   0.22
Worldwide_Gross            0.22
Title                      0.03
Production_Budget          0.03
Release_Date               0.00
dtype: float64

1.3.2 Reshaping the Data for Visualization

To visualize missing values with Altair, we need to reshape the data into a long format where each missing value is a row:

movies_nans = movies.isna().reset_index().melt(
    id_vars='index',
    var_name='column',
    value_name="NaN"
)
movies_nans

	index	column	NaN
0	0	Title	False
1	1	Title	False
2	2	Title	False
3	3	Title	False
4	4	Title	False
...	...	...	...
51211	3196	IMDB_Votes	False
51212	3197	IMDB_Votes	True
51213	3198	IMDB_Votes	False
51214	3199	IMDB_Votes	False
51215	3200	IMDB_Votes	False

51216 rows × 3 columns

1.3.3 Heatmap of Missing Data

This heatmap shows where missing values occur across rows and columns. Patterns may indicate:

• Columns with consistently missing values
• Entire rows with large gaps
• Correlated missingness between variables

To avoid limitations in the number of rows rendered by Altair, we disable the max rows warning:

alt.data_transformers.disable_max_rows()

DataTransformerRegistry.enable('default')

Now we can create the heatmap:

alt.Chart(movies_nans).mark_rect().encode(
    alt.X('index:O'),
    alt.Y('column'),
    alt.Color('NaN')
).properties(
    width=1000
)

This plot can help identify columns or rows with critical data issues.

1.3.4 Dropping Columns with High Missing Rate

In many real-world cases, we may decide to remove columns that have too many missing values. Let’s set a threshold of 70%:

threshold_nan = 70 # in percent
cols_to_drop = nan_percent[nan_percent>threshold_nan].index
cols_to_drop

Index(['US_DVD_Sales'], dtype='object')

These columns have more than 70% missing values and may not be useful for analysis.

1.4 Cleaned Dataset

Finally, we drop the selected columns and inspect the updated dataset:

movies_cleaned = movies.drop(columns=cols_to_drop)
movies_cleaned

	Title	US_Gross	Worldwide_Gross	Production_Budget	Release_Date	MPAA_Rating	Running_Time_min	Distributor	Source	Major_Genre	Creative_Type	Director	Rotten_Tomatoes_Rating	IMDB_Rating	IMDB_Votes
0	The Land Girls	146083.0	146083.0	8000000.0	Jun 12 1998	R	NaN	Gramercy	None	None	None	None	NaN	6.1	1071.0
1	First Love, Last Rites	10876.0	10876.0	300000.0	Aug 07 1998	R	NaN	Strand	None	Drama	None	None	NaN	6.9	207.0
2	I Married a Strange Person	203134.0	203134.0	250000.0	Aug 28 1998	None	NaN	Lionsgate	None	Comedy	None	None	NaN	6.8	865.0
3	Let's Talk About Sex	373615.0	373615.0	300000.0	Sep 11 1998	None	NaN	Fine Line	None	Comedy	None	None	13.0	NaN	NaN
4	Slam	1009819.0	1087521.0	1000000.0	Oct 09 1998	R	NaN	Trimark	Original Screenplay	Drama	Contemporary Fiction	None	62.0	3.4	165.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3196	Zack and Miri Make a Porno	31452765.0	36851125.0	24000000.0	Oct 31 2008	R	101.0	Weinstein Co.	Original Screenplay	Comedy	Contemporary Fiction	Kevin Smith	65.0	7.0	55687.0
3197	Zodiac	33080084.0	83080084.0	85000000.0	Mar 02 2007	R	157.0	Paramount Pictures	Based on Book/Short Story	Thriller/Suspense	Dramatization	David Fincher	89.0	NaN	NaN
3198	Zoom	11989328.0	12506188.0	35000000.0	Aug 11 2006	PG	NaN	Sony Pictures	Based on Comic/Graphic Novel	Adventure	Super Hero	Peter Hewitt	3.0	3.4	7424.0
3199	The Legend of Zorro	45575336.0	141475336.0	80000000.0	Oct 28 2005	PG	129.0	Sony Pictures	Remake	Adventure	Historical Fiction	Martin Campbell	26.0	5.7	21161.0
3200	The Mask of Zorro	93828745.0	233700000.0	65000000.0	Jul 17 1998	PG-13	136.0	Sony Pictures	Remake	Adventure	Historical Fiction	Martin Campbell	82.0	6.7	4789.0

3201 rows × 15 columns

2 Types of Data Analysis in EDA

Understanding the nature of variables and the relationships between them is central to Exploratory Data Analysis (EDA). Depending on the number and type of variables involved, we can classify analysis into three main categories: univariate, bivariate, and multivariate.

Table 1 shows how EDA analysis types vary depending on the number and types of variables involved.

This classification helps guide the selection of appropriate visualization techniques and statistical methods for each case.

Table 1: Types of analysis and variable combinations used in EDA

Analysis Type	Variable Types	Description	Examples
Univariate	Categorical	One qualitative variable	Gender, Product Category
Univariate	Quantitative	One numerical variable	Income, Age, Runtime
Bivariate	Categorical – Categorical	Two qualitative variables	Gender vs Nationality
Bivariate	Categorical – Quantitative	One qualitative and one numerical	Province vs Population
Bivariate	Quantitative – Quantitative	Two numerical variables	Age vs Income
Multivariate	3 or more variables (any mix)	Combination of categorical and/or numerical	Age vs Income by Gender, etc.

2.1 Univariate Analysis: Quantitative

A univariate analysis focuses on examining a single numeric variable to understand its distribution, shape, central tendency, and spread. One of the most common tools for this is the histogram.

In this case, we’ll explore the distribution of the movie runtime (Running_Time_min).

2.1.1 Basic Histogram

We start by creating a histogram to visualize the distribution of running times:

alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('Running_Time_min',bin=alt.Bin(maxbins=30)),
    alt.Y('count()')
).properties(
    title='Histogram of Movie Runtimes (30 bins)'
)

This chart shows how many movies fall into each time interval (bin). However, histograms can look quite different depending on the number and size of bins used.

2.1.2 Effect of Bin Size

Let’s compare how the histogram shape changes with different bin sizes:

histogram_1 = alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('Running_Time_min',bin=alt.Bin(maxbins=8)),
    alt.Y('count()')
)

histogram_2 = alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('Running_Time_min',bin=alt.Bin(maxbins=10)),
    alt.Y('count()')
)

histogram_1 | histogram_2

Even though both plots use the same data, the choice of bin size changes the visual interpretation. A small number of bins may hide details, while too many bins can make it harder to spot trends.

2.1.3 Density plots, or Kernel Density Estimate (KDE)

Density plots offer a smoothed alternative to histograms. Instead of using rectangular bins to count data points, they estimate the probability density function by placing bell-shaped curves (kernels) at each observation and summing them.

This approach helps reduce the visual noise and jaggedness that can occur in histograms and gives a clearer picture of the underlying distribution.

alt.Chart(movies_cleaned).transform_density(
    'Running_Time_min',
    as_=['Running_Time_min','density'],
).mark_area().encode(
    alt.X('Running_Time_min'),
    alt.Y('density:Q')
).properties(
    title="Movies runtime"
)

2.1.4 Grouped Density plot

We can also compare distributions across groups by splitting the KDE by a categorical variable using the groupby parameter. This helps us see how the distribution differs between categories, such as genres.

selection = alt.selection_single(fields=['Major_Genre'], bind='legend')

alt.Chart(movies_cleaned).transform_density(
    'Running_Time_min',
    groupby=['Major_Genre'],
    as_=['Running_Time_min', 'density'],
).mark_area(opacity=0.5).encode(
    alt.X('Running_Time_min'),
    alt.Y('density:Q', stack=None),
    alt.Color('Major_Genre'),
    opacity=alt.condition(selection, 
        alt.value(1), 
        alt.value(0.05)
    )
).add_selection(
    selection
).properties(
    title="Movies Runtime by Genre (Interactive Filter)"
).interactive()

The transparency (opacity=0.5) allows us to observe overlapping distributions and ensures that small density areas are not completely hidden behind larger ones.

From this plot, we can observe, for example, that Drama movies have runtimes nearly as long as the longest Adventure movies, even though their overall distributions differ.

2.2 Bivariate Analysis: Categorical vs Quantitative

Bivariate analysis examines the relationship between two variables. In this case, we focus on one categorical variable (e.g., genre) and one quantitative variable (e.g., revenue), which is a very common scenario in exploratory data analysis.

This type of analysis is useful to: - Compare average or median values across categories. - Detect outliers or high-variance groups. - Understand distributional differences across categories.

Below are several effective visualizations for this analysis.

2.2.1 Basic Barchart

Bar charts are effective for comparing aggregated values (like the mean) across different groups. However, they hide the distribution and variation within each group.

alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('mean(Worldwide_Gross)'),
    alt.Y("Major_Genre")
).properties(
    title="Average Worldwide Gross by Genre"
)

This bar chart shows the mean Worldwide Gross per genre. It is useful for identifying which genres are more profitable on average, but does not show how spread out the data is.

2.2.2 Tick Plot

To visualize individual data points, we use a tick plot. This helps uncover variability within genres and detect outliers.

alt.Chart(movies_cleaned).mark_tick().encode(
    alt.X('Worldwide_Gross'),
    alt.Y("Major_Genre"),
    alt.Tooltip('Title:N')
).properties(
    title="Individual Gross per Movie by Genre"
)

2.2.3 Heatmaps

Heatmaps can summarize the frequency of data points across both axes (quantitative and categorical) using color intensity. It’s particularly useful for spotting patterns without getting overwhelmed by individual points.

alt.Chart(movies_cleaned).mark_rect().encode(
    alt.X('Worldwide_Gross',bin=alt.Bin(maxbins=100)),
    alt.Y("Major_Genre"),
    alt.Color('count()'),
    alt.Tooltip('count()')
).properties(
    title="Heatmap of Movie Counts by Gross and Genre"
)

This heatmap shows how frequently movies from each genre fall into different revenue ranges.

2.2.4 Boxplot

Boxplots are useful for comparing distributions across categories and identifying outliers. Boxplots summarize a distribution using five statistics:

• Median (Q2)
• First Quartile (Q1)
• Third Quartile (Q3)
• Lower Whisker (Q1 - 1.5 × IQR)
• Upper Whisker (Q3 + 1.5 × IQR)

alt.Chart(movies_cleaned).mark_boxplot().encode(
    alt.X('Worldwide_Gross'),
    alt.Y("Major_Genre")
).properties(
    title="Boxplot of Worldwide Gross by Genre"
)

2.2.5 Side-by-side: Boxplot and Bar Chart

To contrast aggregated values (bar chart) with the full distribution (boxplot), we can display them together:

bar = alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('mean(Worldwide_Gross)'),
    alt.Y("Major_Genre")
)

box = alt.Chart(movies_cleaned).mark_boxplot().encode(
    alt.X('mean(Worldwide_Gross)'),
    alt.Y("Major_Genre")
)

box | bar

This comparison reveals whether the mean is a good representative of the genre, or whether the data is skewed or contains outliers that affect the average

2.3 Bivariate Analysis: Quantitative vs Quantitative

When analyzing two quantitative (numerical) variables simultaneously, we aim to discover possible relationships, trends, or correlations. This type of bivariate analysis can reveal whether increases in one variable are associated with increases or decreases in another (positive or negative correlation), or if there’s no relationship at all. The most common and intuitive visualization for this is the scatterplot.

2.3.1 Scatterplots

Scatter plots are effective visualizations for exploring two-dimensional distributions, allowing us to identify patterns, trends, clusters, or outliers.

Let’s start by visualizing how movies are rated across two popular online platforms:

• IMDb
  
• Rotten Tomatoes

Are movies rated similarly on different platforms?

alt.Chart(movies_cleaned).mark_point().encode(
    alt.X('IMDB_Rating'),
    alt.Y('Rotten_Tomatoes_Rating')
).properties(
    title="IMDB vs Rotten Tomatoes Ratings"
)

2.3.2 Scatterplot Saturation

Scatterplots can become saturated when too many points overlap in a small area of the chart, making it difficult to distinguish dense regions from sparse ones. For example, when plotting financial variables like production budget versus worldwide gross:

saturated = alt.Chart(movies_cleaned).mark_point().encode(
    alt.X('Production_Budget'),
    alt.Y('Worldwide_Gross')
).properties(
    title="Saturated Scatterplot: Budget vs Gross"
)
saturated

2.3.3 Using Binned Heatmap to Reduce Saturation

To address saturation, we can bin both variables and use a heatmap where the color intensity represents the number of movies that fall into each rectangular region of the grid. This makes dense areas more interpretable

heatmap_scatter = alt.Chart(movies_cleaned).mark_rect().encode(
    alt.X('Production_Budget', bin=alt.Bin(maxbins=60)),
    alt.Y('Worldwide_Gross', bin=alt.Bin(maxbins=60)),
    alt.Color('count()'),
    alt.Tooltip('count()')
).properties(
    title="Binned Heatmap: Budget vs Gross"
)
heatmap_scatter

2.3.4 Side-by-side Comparison

Compare the raw scatterplot with the heatmap representation:

saturated | heatmap_scatter

2.4 Bivariate Analysis: Categorical vs Categorical

When working with two categorical variables, bivariate analysis helps us understand how categories from one variable relate or are distributed across the other. For example, we might want to know how different movie genres are rated according to the MPAA rating system. Visualization techniques like grouped bar charts and faceted plots can reveal patterns, associations, or class imbalances.

2.4.1 Basic Faceted Bar Chart

We begin by exploring how movies are rated (MPAA_Rating) across different genres (Major_Genre). A faceted bar chart allows us to visualize this relationship by plotting a bar chart per genre, helping to identify genre-specific rating distributions.

alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('count()'),
    alt.Y('MPAA_Rating'),
    alt.Color('MPAA_Rating')
).facet(
    'Major_Genre'
)

2.4.2 Vertical Faceting for Alignment

Faceting horizontally can make comparisons across genres harder when the x-axis is misaligned. By specifying columns=1, we lay out the facets vertically, making it easier to compare counts across genres.

alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('count()'),
    alt.Y('MPAA_Rating'),
    alt.Color('MPAA_Rating')
).facet(
    'Major_Genre',
    columns=1
)

2.4.3 Dependent vs Independent Axis Scaling

By default, facet plots share the same x-axis scale (dependent scale), which allows for easier comparison across panels. However, when the number of observations varies greatly between genres, this shared scale can compress some charts.

We can instead use independent x-axis scaling for each facet. This highlights the relative distribution within each genre.

shared_scale = alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('count()'),
    alt.Y('MPAA_Rating'),
    alt.Color('MPAA_Rating')
).facet(
    'Major_Genre',
    columns=4
)

independent_scale = alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X('count()'),
    alt.Y('MPAA_Rating'),
    alt.Color('MPAA_Rating')
).facet(
    'Major_Genre',
    columns=4
).resolve_scale(x='independent')

shared_scale | independent_scale

The left panel (shared scale) makes absolute comparisons between genres, while the right panel (independent scale) makes within-genre comparisons more readable.

2.4.4 Heatmaps

Heatmaps are effective for visualizing the relationship between two categorical variables when the goal is to display counts or frequency of occurrences. They map the number of observations to color, providing an intuitive view of which category pairs are most or least common.

We can enhance this basic representation by also using marker size, combining both color intensity and circle area to represent counts more effectively. This dual encoding can improve interpretation, especially when printed in grayscale or when there are subtle color differences.

heatmap_color = alt.Chart(movies_cleaned).mark_rect().encode(
    alt.X('MPAA_Rating'),
    alt.Y('Major_Genre', sort='color'),
    alt.Color('count()')
).properties(
    title="Heatmap with Color (Count of Movies)"
)

heatmap_size = alt.Chart(movies_cleaned).mark_circle().encode(
    alt.X('MPAA_Rating'),
    alt.Y('Major_Genre', sort='color'),
    alt.Color('count()'),
    alt.Size('count()')
).properties(
    title="Heatmap with Color + Size (Count of Movies)"
)

heatmap_color | heatmap_size

2.5 Multivariate Analysis

Multivariate analysis helps us understand the interactions and relationships among multiple variables simultaneously. In the context of numerical features, it is useful to explore pairwise distributions, correlations, and detect potential clusters or anomalies.

When the number of variables is large, repeated charts such as histograms or scatter plot matrices help us summarize patterns efficiently and consistently across all numerical dimensions.

2.5.1 Repeated Histograms for Numerical Columns

We first identify and isolate all numerical columns from the dataset. Then we repeat a histogram for each of these columns to understand the individual distributions. This overview is helpful to detect skewness, outliers, or binning decisions that affect how data is grouped visually.

# Select only numerical columns
numerical_columns = movies_cleaned.select_dtypes('number').columns.tolist()

alt.Chart(movies_cleaned).mark_bar().encode(
    alt.X(alt.repeat(),type='quantitative',bin=alt.Bin(maxbins=30)),
    alt.Y('count()')
).properties(
    width=150,
    height=150
).repeat(
    numerical_columns,
    columns=4
)

2.5.2 Scatter Plot Matrix (Pairplot)

A scatter plot matrix shows the pairwise relationships between all numerical variables. This is a common exploratory tool to detect:

• Correlations between variables
• Outliers or clusters
• Relationships useful for prediction models (e.g., to predict rating or budget)

We focus especially on the plots below the diagonal, as they are not duplicated.

alt.Chart(movies_cleaned).mark_point().encode(
    alt.X(alt.repeat('column'),type='quantitative'),
    alt.Y(alt.repeat('row'),type='quantitative'),
    alt.Tooltip('Title:N')
).properties(
    width=100,
    height=100
).repeat(
    column=numerical_columns,
    row=numerical_columns
)

2.5.3 Heatmap Matrix

When scatter plots become too saturated (many overlapping points), heatmaps offer a better alternative by binning the numeric values and encoding the count in color intensity.

alt.Chart(movies_cleaned).mark_rect().encode(
    alt.X(alt.repeat('column'),type='quantitative',bin=alt.Bin(maxbins=30)),
    alt.Y(alt.repeat('row'),type='quantitative',bin=alt.Bin(maxbins=30)),
    alt.Color('count()'),
    alt.Tooltip('count()')
).properties(
    width=100,
    height=100
).repeat(
    column=numerical_columns,
    row=numerical_columns
).resolve_scale(
    color='independent'
)

To gain deeper insights into the dataset, it’s important to analyze how numerical variables behave across different categories. This type of multivariate analysis allows us to:

• Compare distributions across categories
• Detect outliers within categories
• Observe central tendency (median, quartiles) and spread (range, IQR)

Boxplots are particularly effective for this purpose. In the following visualizations, we explore these relationships by repeating plots across combinations of categorical and numerical features.

2.5.4 Filter Categorical Columns

First, we select the relevant categorical columns, excluding identifiers and text-heavy variables like movie titles or director names.

categorical_columns =  movies_cleaned.select_dtypes('object').columns.to_list()

categorical_columns_remove = ['Title','Release_Date','Distributor','Director']

categorical_filtered = [col for col in categorical_columns if col not in categorical_columns_remove]

2.5.5 Repeated Boxplots: Categorical vs Numerical

We repeat boxplots using combinations of categorical (rows) and numerical (columns) features. This matrix layout gives a clear visual overview of how numerical values are distributed within each category.

alt.Chart(movies_cleaned).mark_boxplot().encode(
    alt.X(alt.repeat('column'),type='quantitative'),
    alt.Y(alt.repeat('row'),type='nominal'),
    alt.Size('count()')
).properties(
    width=200,
    height=200
).repeat(
    column=numerical_columns,
    row=categorical_filtered
)

2.5.6 Faceted Boxplots

For more focused analysis, we can facet the boxplots using a specific categorical variable like MPAA_Rating, and repeat the chart by different categorical rows. This lets us keep the numerical axis fixed while comparing how categories vary across different classes (e.g., movie ratings).

alt.Chart(movies_cleaned).mark_boxplot().encode(
    alt.X('Running_Time_min', type='quantitative'),
    alt.Y(alt.repeat('row'),type='nominal'),
    alt.Size('count()'),
    alt.Tooltip('Title:N')
).properties(
    width=100,
    height=100
).facet(
    column='MPAA_Rating'
).repeat(
    row=categorical_filtered
)